General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.
翻译:一般可视化建议系统通常会自动为数据集做出设计决定。 但是, 大部分系统只能进行毫无意义的可视化, 但却无法推荐有针对性的结果。 本文提供了任务导向的可视化建议系统Tlevel Vis, 这个系统允许用户在界面上精确地选择任务。 我们首先通过在学术界和工业界的调查, 将任务基础与18项传统分析任务进行总结。 在这个基础上, 我们维持一个规则基础, 它将经验智慧与我们有针对性地模拟分析任务。 然后, 我们基于规则的方法通过响应设定的编程来罗列所有候选人的可视化。 之后, 生成的图表可以按四级排序 。 此外, 我们引入基于任务的组合建议战略, 利用一组可视化来通过协作的方式对数据集进行简要的查看 。 最后, 我们通过一系列使用案例和用户研究来评估任务视图 。